Abstract Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-
world scenarios. Despite the great success, the architecture design of GNNs heavily relies …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

Recently, Neural Architecture Search (NAS) for GNN has received increasing popularity as it
can seek an optimal architecture for a given new graph. However, the optimal architecture is …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data
such as social network data and biological data. Despite their success, the design of graph …

Abstract Graph Neural Networks (GNNs) have become one of the indispensable tools to
learn from graph-structured data, and their usefulness has been shown in wide variety of …

Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics
such as node classification, link prediction and graph clustering. Many GNN training …

Graph Neural Networks have emerged as a useful tool to learn on the data by applying
additional constraints based on the graph structure. These graphs are often created with …

Learning from graph and relational data plays a major role in many applications including
social network analysis, marketing, e-commerce, information retrieval, knowledge modeling …

Graph neural architecture search has shown great potentials for automatically designing
graph neural network (GNN) architectures for graph classification tasks. However, when …